In this paper, a new Discriminative Proposals Sampling (DiPS) method is proposed to leverage localization information from SSTs for accurate WSOL. In particular, class token maps are extracted from the top block of pretrained a SST. Given a limited number of high-quality attention maps (class tokens) covering different objects, we employ a CNN classifier to localize potential discriminative regions (see Fig. REF (a)), and sample pixel-wise pseudo-labels from these regions to train a U-Net style localization network. To benefit from these tokens, we propose a method for collecting appropriate pseudo-labels, and sampling foreground/background regions from them to train our localization network such that a particular object can be localized with high confidence (Fig.REF ). More specifically, with the help of a CNN classifier, we identify sampling areas for building effective pseudo-labels by determining foreground and background regions in class tokens obtained from SST. From these areas, foreground pixels are sampled, while the entire image was used to sample pixels from background regions according to the criteria defined in Section . Using these pseudo-pixels, the localization network is trained by optimizing log-barrier [1]} method on the selected pixels. Additionally, the localization network parameters are regularized by class and CRF loss [2]} to generating threshold insensitive. In the end, only localization network is retained for inference of localization maps.
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